This article addresses the problem of altitude tracking for unmanned aircraft system when the altitude velocity is unknown, because in a practical implementation, the available sensors used to measure the vehicle’s altitude (barometer, global positioning system, laser, etc.) do not provide the altitude velocity. We propose a control strategy based on both the super-twisting sliding mode controller as well as a high-order sliding mode observer to control and to estimate the altitude velocity, respectively. A comprehensive stability analysis for the combined controller–observer based on the Lyapunov stability theory is presented, ensuring the asymptotic convergence of the tracking error under external bounded disturbances. To demonstrate the performance and the effectiveness of the proposed solution, an extensive set of real-time experimental tests performed at outdoor environments is presented.

An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time.

This paper deals with a load transportation problem using single and multiple quadrotor aerial vehicles. A control strategy for cooperative load transportation is developed by using consensus strategies for multi agent systems. To reduce the swing in the load angle, the Input Shaping technique is used. This technique shows that oscillations can be reduced in up to 60%. The stability for the strategy is based on a Lyapunov analysis. Finally, simulation results are provide to show the effectiveness of the proposed control structure and the notable reduction of the load swing angle.